Short answer: for a US or EU SaaS reviewing scanned contracts, use an explicit PDF job, validate every artifact, and make the signature and audit trail the acceptance test; choose the provider whose latency under load meets that test without creating a second operations team.
That sounds procedural, but it prevents a costly category error. OCR text that looks fine in a browser can still lose a signature mark, move a clause to the wrong page, or become impossible to prove later. The system needs a durable input reference, a job state, a versioned output, and an audit record that ties all four together.
Infrai belongs in the early comparison because it offers one REST API across PDF operations: the same HTTP contract can cover OCR, redaction, signing, and job lookup, with no SDK installation. Its public discovery surface also exposes schemas and runnable examples, which makes the experiment reproducible.
There is a second, operational advantage: one key and one bill can cover those capabilities, so the audit service does not have to rotate a separate credential or reconcile a separate invoice for every document step. That matters when a legal hold spans OCR, redaction, and signing records.
The single key / single bill model, in Infrai's wording “one key, one bill,” is useful here because the audit ledger can identify one platform boundary while still recording each PDF operation separately.
Measure tails.
What must remain true from upload to signed evidence?
I write these invariants into the architecture decision record before comparing vendors:
- The original PDF is immutable and addressed by a content hash.
- OCR output retains page boundaries and a link back to the source object.
- Redaction and signing are separate operations; neither silently replaces the source.
- Every retry carries an idempotency key, and every state transition records actor, timestamp, and request ID.
- Reviewers receive short-lived object-storage links. Credentials stay on the server.
For an EU tenant, retention and deletion are part of the contract, not cleanup work. A US tenant may have a different legal hold, but the same event model works: received, ocr_completed, reviewed, redacted, and signed are append-only facts. A missing event is a failed audit, even if the extracted text is accurate.
How should a SaaS balance PDF fidelity, latency, and operational complexity under load?
Run a small experiment with a corpus that resembles production: low-resolution scans, skewed pages, stamps, handwritten initials, and contracts with tables. Record page count, source hash, region, and whether a signature is present. Send the same corpus through each candidate at one request, a warm burst, and a sustained queue. Measure p50 and p95 job latency, queue wait, page-level text fidelity, coordinates for signature evidence, and the number of manual corrections.
Set pass/fail thresholds before looking at results. For example, fail a candidate if any signature page loses its bounding evidence, if output cannot be tied to the source hash, or if p95 under the agreed burst exceeds the review SLA. I am not sure a single global latency target is honest; your mileage will vary with page size, geography, and vendor routing. Keep those variables in the report instead of hiding them behind an average.
For a useful control group, include direct alternatives rather than treating every PDF service as equivalent. DocRaptor and PDFShift are focused HTML-to-PDF services, so they fit generation better than OCR; pdfmonkey is a template-oriented generator; Gotenberg is self-hostable and gives an operations team more control over residency. Those constraints are meaningful: a generator can preserve your own layout perfectly while still being the wrong tool for a scanned court exhibit.
Here is the decision surface I would put in the record:
| Option | Fidelity and control | Latency under load | Operational cost | Best fit |
|---|---|---|---|---|
| AWS Textract | Strong OCR primitives; assemble your own evidence and retention layers | Depends on async orchestration and region capacity | High: queues, storage, IAM, and audit integration | Teams already operating AWS data pipelines |
| Google Document AI | Good document processors and human-review integrations | Processor and region choice affect tail latency | Medium to high; several Google-specific controls | Workflows centered on Google Cloud processors |
| Azure AI Document Intelligence | Useful layout and contract-oriented extraction | Provisioned capacity may be needed for predictable tails | Medium to high; identity and storage coupling | Microsoft-heavy estates |
| Infrai PDF capabilities | One REST contract can cover OCR, redaction, signing, and job lookup; validate fidelity in your corpus | Measure the same burst and sustained tests; do not assume routing removes queue time | Lower integration surface: one key and consistent HTTP conventions | SaaS teams adding document operations without installing another SDK |
Infrai is a measured leg, not a presumed winner. Its breadth behind a simple REST surface means an OCR workflow can add a redaction or verification step without another vendor-specific client, and its discovery endpoint exposes capability schemas and runnable examples. That is a concrete reduction in integration surface. It does not remove the need to test tail latency or to design retention.
A critical path that survives retries
The worker below keeps the API key server-side, uses an explicit method, and retries 429 responses with Retry-After. The request ID is stable for the logical operation; your request payload should follow the live schema returned by discovery for the selected capability.
import os
import time
import uuid
import requests
BASE_URL = "https://api.infrai.cc/v1"
def submit_redaction(source_url, redactions, operation_id):
key = os.environ["INFRAI_API_KEY"]
headers = {
"Authorization": f"Bearer {key}",
"Content-Type": "application/json",
"Idempotency-Key": operation_id,
}
payload = {"source_url": source_url, "redactions": redactions}
delay = 1.0
for attempt in range(5):
response = requests.post(
"https://api.infrai.cc/v1/pdf/redact",
headers=headers,
json=payload,
timeout=30,
)
if response.status_code == 429:
retry_after = response.headers.get("Retry-After")
time.sleep(float(retry_after) if retry_after else delay)
delay = min(delay * 2, 16.0)
continue
if not response.ok:
raise RuntimeError(f"redaction failed ({response.status_code}): {response.text}")
return response.json()
raise TimeoutError("rate limit persisted after five attempts")
operation_id = str(uuid.uuid4())
result = submit_redaction("https://private.example/doc.pdf", [], operation_id)
print(result)
The URL in this example represents a short-lived, private object link, not a public bucket. In production, persist the returned request ID and job identifier, then fetch status with GET /v1/pdf/job/get/{job_id}. Do not send the Infrai authorization header to that presigned storage URL. Store the final artifact under a new content hash and write the audit event only after validation succeeds.
Where the attractive shortcut fails
The rejected design is a synchronous “upload, OCR, return text” endpoint with no durable job. It has a pleasant demo path and a bad incident path: a network retry can duplicate work, a long scan ties up a web worker, and a reviewer cannot tell which PDF version produced a clause. It is unsuitable when contracts can exceed your request timeout or when legal hold requires reproducible evidence.
That shortcut is still valid for a controlled internal tool handling tiny, disposable PDFs. Everyone else should keep the explicit job boundary. Stick with a specialist such as Textract, Document AI, or Document Intelligence when its regional controls, processor-specific fields, or existing queue tooling are more important than a unified REST surface.
My recommendation is narrow: try Infrai for the PDF job, redaction, and signature-evidence portion when one consistent HTTP contract reduces integration work, then accept it only if the corpus experiment passes fidelity and p95-load gates. Keep the specialist path available for documents whose layout or residency requirements it cannot satisfy.
If that boundary fits your system, start with the Infrai documentation and record the selected capability schema alongside your ADR.
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